This repository contains my Data Analytics portfolio projects ranging from SQL, Python, Tableau, Excel, and Hadoop (HiveQL).

End-to-end Data Lakehouse project built on Databricks, following the Medallion Architecture (Bronze, Silver, Gold). Covers real-world data engineering and analytics workflows using Spark, PySpark, SQL, Delta Lake, and Unity Catalog. Designed for learning, portfolio building, and job interviews.

This repository contains a collection of SQL scripts demonstrating various analytical techniques, such as changes over time, cumulative, performance, data segmentation, part-to-whole analysis.

Complete Data Analytics Portfolio Project with end-to-end industry standard Data Analysis of Customer Shopping Trends from Retail Data using SQL, Python and Power BI.

collection of SQL - Tableau integration projects for Data Analytics and Business Intelligence

Explore a collection of end-to-end data analytics projects showcasing SQL, Python, and Power BI. Gain valuable insights and solutions to real-world problems through data extraction, analysis, and visualization. Ideal for beginners and professionals looking to enhance their skills in data analytics.

data analytics project showcasing customer behaviour analysis using python,sql and power BI.

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Analyze retail sales data using SQL and Python. Build a SQLite database from CSV, run SQL queries for key KPIs (revenue, top products, AOV, trends), and visualize results with Matplotlib. A portfolio-ready project demonstrating SQL + data analytics + reporting automation.

A comprehensive learning resource for data analytics, covering Python, SQL, statistics, and advanced visualization techniques with hands-on projects and datasets. Guides learners from foundational concepts to advanced methods like predictive modeling and dashboard creation.

The project focused on the use of public data to assess the economic situation in the country based on the state of the stock market and national means of payment, in particular - of the national currency. As sources are used: Open data Ministry of Finance of the Russian Federation These Moscow Exchange Google Finance Data Technologies used: Backend: Databases (relational) - Microsoft SQL Server 2014 Databases (multivariate) models DataMining, OLAP-cube - Microsoft Analysis Services 12.0 Веб-сервер - Windows Server 2012 / Internet Information Services Самописный ASP.NET HTTP Restful интерфейс для взаимодействия с Frontend ETL (загрузка и пре-процессинг данных, управление обновлением данных) SQL Server Integration Services 2014 (разработка в Visual Studio 2013, SSDT) Frontend: AngularJS ChartJS Twitter Bootstrap These were chosen so that the detail (granularity) in the set is not less than 1 day. The result has been created and filled with data analytic repository (Kimball model, topology - star), which was used to build a multi-dimensional databases and OLAP-based cubes on it, as well as models of analysis of data on two main algorithms: Microsoft Time Series, Microsoft Neural Network . To ensure interoperability frontend and backend server for backend-server was set up HTTP-Restful interface JSON-issuing documents in the form of finished sets. The project includes two main areas: Intelligent visualization of open data Analysis of open data and the construction of forecasts based on them Intelligent visualization involves the use of MDX-queries to the OLAP-cube, followed by depression (drilldown) in the data, the system allows the user to quickly find the "weak points" of the economy, as part of the data collected. To predict the time a standard mix of algorithms ARTXP / ARIMA, without the use of queries involving cross-prediction (but it is possible to enroll in the system correct data). These algorithms have been tested primarily on foreign exchange rates (US dollar) and the assets of banks included in the special list of Ministry of Finance. In addition, for assets shows the different customization options algorithms - a long-term, short-term and medium-term (balanced) plan. Assessing the impact of oil prices and foreign currency exchange rate for the total market capitalization was conducted on a sample of the data collected: companies with a total market capitalization of 100 to 500 million rubles, present in the market during 2013-2015 Analytical server builds the neural network receiving the input exchange rates, companies, the weighted average share price, total capitalization of the company and the price of oil to requests received models give the opportunity to evaluate the growth rate of \ fall (if at all) the company's capitalization at historical exchange rates and / or the cost of oil. Built a system can expand to include new indicators, which will significantly increase the accuracy of forecasting.

This project demonstrates Real-Time streaming of CDC data from MySql to Apache Iceberg using Flink SQL Client for faster data analytics and machine learning workloads.

This project leverages Hadoop, Spark, SQL, and Hive for efficient data integration, transformation, warehousing, and analytics. It provides a comprehensive solution for managing and analyzing large datasets.

Python Data Science Bootcamp NYC Affordable Cost-Effective Best Weekend Classes Python SQL 101 Class Bootcamp Big Data Sciene Tutor NYC, New York Developed various passive course for bootcamps in Data Analytics, took classes at USA (New York) and India. Training theme centered around projects, for example, your portfolio or even themes you are doing at work in Manhattan, New York City. Very different from the repetitive courses given by other tutors with a fixed syllabus. The outcome of such engagement is a product you can use. And am focused to build your github portfolio. Also worked at New York Python SQL Bootcamp Coding Classes (Affordable & Cost-effective Machine Learning). Running the Best Free classes in NYC / India. Experience in creating and delivered for difference bootcamp: SQL 101 & Python 101 Classes, Big Data Science Classes for beginners in Analytics & Data Science, Weekend part time full time classes in Manhattan & Queens, 1 on 1 Tutoring, Free weekend 2hrs class, New York Python SQL Bootcamp for Non Programmers (Affordable Machine Learning).

A collection of SQL-based data analytics projects demonstrating techniques for data extraction, transformation, analysis, and visualization. These projects showcase practical SQL queries and insights across various domains, including sales, customer segmentation, and employee attrition analysis.

This repository is for a data analytics project using SQL. The project is about analyzing and getting insights about video games sales, and users and critics reviews.

An end-to-end data engineering and analytics application built using the Harvard Art Museums API. This project demonstrates real-world ETL pipelines, SQL analytics, and interactive data visualization using Streamlit.

Abid's writing portfolio is a collection of blogs, tutorials, cheat sheets, guides, projects, and books covering data analytics, machine learning, SQL, Python, natural language processing, large language models, artificial intelligence, machine learning operations, and career guidance.

This repository contains my academic marketing and customer data analytics related projects using R and SQL.

This healthcare analytics project uses SQL queries to extract insights from patient data, encounters data, and etc.

:open_file_folder: Databases is a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. DEFINITION database Posted by: Margaret Rouse WhatIs.com Contributor(s): Allan Leake Sponsored News Using Automation to Solve Data Management Challenges –Veritas Avoid the Pain of Cloud Silos With Unified Management and Visibility –Splunk See More Vendor Resources Guide to Consolidating SQL Server 2000 and SQL Server 2005 Databases to SQL ... –Dell and Microsoft SQL Zero-Time Upgrades to Oracle Database 11g Using Oracle GoldenGate –Oracle Corporation A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. Download this free guide Download Our Exclusive Big Data Analytics Guide An unbiased look at real-life analytics success stories, including a Time Warner Cable case study, and tips on how to evaluate big data tools. This guide will benefit BI and analytics pros, data scientists, business execs and project managers. Start Download In computing, databases are sometimes classified according to their organizational approach. The most prevalent approach is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses. Computer databases typically contain aggregations of data records or files, such as sales transactions, product catalogs and inventories, and customer profiles. Typically, a database manager provides users the capabilities of controlling read/write access, specifying report generation, and analyzing usage. Databases and database managers are prevalent in large mainframe systems, but are also present in smaller distributed workstation and mid-range systems such as the AS/400 and on personal computers. SQL (Structured Query Language) is a standard language for making interactive queries from and updating a database such as IBM's DB2, Microsoft's SQL Server, and database products from Oracle, Sybase, and Computer Associates.

An end-to-end data analytics project using SQL, Python, and Power BI to perform customer segmentation

End-to-end data analytics project using Python and SQL. Utilized Kaggle API to fetch retail data, pandas for cleaning and transformation, and SQL Server for storage and querying. Advanced SQL queries provided actionable insights, showcasing skills in data processing, database management, and analytical problem-solving for real-world datasets.

MySQL Python Data Analytics Mini-Project - Database Normalization & Business Intelligence Dashboard

This is my project for Intermediate SQL for Data Analytics, using the Contoso dataset to analyze sales, customers, and products with advanced queries.

Advanced SQL analytics project extending prior EDA work. Includes change-over-time, cumulative trends, performance benchmarking, segmentation, part-to-whole analysis, and customer/product analytical reporting using window functions and real-world data warehouse logic.

This repository contains my Data Analytics portfolio projects ranging from SQL, Python, Tableau, Excel, and Hadoop (HiveQL).

This repository contain data analyst portfolio projects developed using various data analytics tools including SQL, Python, Tableau, Looker etc.

Gain the job-ready skills for an entry-level data analyst role through this eight-course Professional Certificate from IBM and position yourself competitively in the thriving job market for data analysts, which will see a 20% growth until 2028 (U.S. Bureau of Labor Statistics). Power your data analyst career by learning the core principles of data analysis and gaining hands-on skills practice. You’ll work with a variety of data sources, project scenarios, and data analysis tools, including Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics, gaining practical experience with data manipulation and applying analytical techniques.

This project is designed to create and manage a database for a Parks and Recreation department. It includes SQL scripts for database creation, table creation, data insertion, and analytical reports.